Randomized controlled trial for assessment of Internet of Things system to guide intensive glucose control in diabetes outpatients: Nagoya Health Navigator Study protocol

• Published in: Nagoya journal of medical science 2017-08, Vol.79 (3), p.323-329
• Main Authors: Onoue, Takeshi, Goto, Motomitsu, Kobayashi, Tomoko, Tominaga, Takashi, Ando, Masahiko, Honda, Hiroyuki, Yoshida, Yasuko, Tosaki, Takahiro, Yokoi, Hisashi, Kato, Sawako, Maruyama, Shoichi, Arima, Hiroshi
• Format: Article
• Language: English
• Subjects: Aged; Blood Glucose - analysis; Diabetes Mellitus, Type 2 - blood; Female; Glycated Hemoglobin A - metabolism; Humans; Internet; Male; Middle Aged; Monitoring, Physiologic - methods; Original Paper; Wearable Electronic Devices
• ISSN: 0027-7622; 2186-3326
• DOI: 10.18999/nagjms.79.3.323

The Internet of Things (IoT) allows collecting vast amounts of health-relevant data such as daily activity, body weight (BW), and blood pressure (BP) automatically. The use of IoT devices to monitor diabetic patients has been studied, but could not evaluate IoT-dependent effects because health data were not measured in control groups. This multicenter, open-label, randomized, parallel group study will compare the impact of intensive health guidance using IoT and conventional medical guidance on glucose control. It will be conducted in outpatients with type 2 diabetes for a period of 6 months. IoT devices to measure amount of daily activity, BW, and BP will be provided to IoT group patients. Healthcare professionals (HCPs) will provide appropriate feedback according to the data. Non-IoT control, patients will be given measurement devices that do not have a feedback function. The primary outcome is glycated hemoglobin at 6 months. The study has already enrolled 101 patients, 50 in the IoT group and 51 in the non-IoT group, at the two participating outpatient clinics. The baseline characteristics of two groups did not differ, except for triglycerides. This will be the first randomized, controlled study to evaluate IoT-dependent effects of intensive feedback from HCPs. The results will validate a new method of health-data collection and provision of feedback suitable for diabetes support with increased effectiveness and low cost.